RHist: adaptive summarization over continuous data streams

  • Authors:

  • Lin Qiao;Divyakant Agrawal;Amr El Abbadi

  • Affiliations:

  • University of California, Santa Barbara;University of California, Santa Barbara;University of California, Santa Barbara

  • Venue:
  • Proceedings of the eleventh international conference on Information and knowledge management
  • Year:
  • 2002

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Abstract

Maintaining approximate aggregates and summaries over data streams is crucial to handle the OLAP query workload that arises in applications, such as network monitoring and telecommunications. Furthermore, since the entire data is not available at all times the maintenance task must be done incrementally. We show that R(elaxed)Hist(ogram) is an appropriate summarization under data stream scenario. In order to reduce query estimation errors, we propose adaptive approaches which not only capture the data distribution, but also integrate independent query patterns. We introduce a workload decay model to efficiently capture global workload information and ensure that the query patterns from the recent past are weighted more than queries that are further in the past. We verify experimentally that our approach successfully adapts to continuously changing workload as well as data streams.